Engine control systems may use various calibration tables and maps to optimize engine and powertrain output as operating conditions change over a drive cycle. For example, vehicle systems may be pre-installed with engine maps that are used by the engine control system to determine how to schedule the various actuators. The calibration maps and tables may be populated with data gathered during engine and powertrain design, testing, and experimentation. In addition, engine control systems may be enabled to adapt and update the calibration tables with measurements and feedback data.
However, with powertrains and combustion engines becoming increasingly complex, there may be many degrees of freedom to optimize engine and powertrain output. For example, there may be various combinations of variable cam timing, variable induction system, variable valve lift, etc. that are possible. In engines with complex systems, it may take a significantly long time (e.g., more than a year of test time) to map out all the possible combinations of operation. Some vehicle systems may be configured to self-calibrate. Therein, in-cylinder pressure sensors may be used to self-calibrate the engine from a crude (initial) engine map. Since the combustion process is quite variable, multiple measurements have to be made at fairly steady state conditions to obtain reliable sensor output averages that can be used with confidence to update the engine map and to determine control actions. However, emissions fuel economy and drive traces tend to be transient in nature, spending little time at steady state conditions where adaptations could be made. As a result, self-calibrating vehicles may require many driving cycles to complete the calibration.
In another example, as shown by Lockwood et al. in US Patent Application 2013/0184966, data captured on-board a vehicle during engine operation can be processed by an on-board controller as well as by an off-board controller (such as an off-board cloud computing system). This allows for less computation intensive on-board processing of parameters (e.g., en masse adaptations) and concurrent more computation intensive off-board processing of parameters (e.g., individual adaptations). The concurrent processing allows for faster population of a calibration table while maintaining the processing power and memory configuration of the on-board vehicle control system.
However, the inventors herein have recognized that even with the self-calibration and the off-board processing, calibration tables may not be sufficiently populated in a time-efficient manner. In addition to the long times taken for the self-calibrating and off-board processing vehicles, there may be regions of the engine and powertrain calibration maps that remain insufficiently populated based on the vehicle operator's driving style. For example, aggressive drivers may have high speed and high load regions of their calibration maps well defined while other operating regions are not. As another example, a driver always operating in hot and dry climate regions may trot have sufficient calibration adaptations for wet and cold ambient conditions. As such, before a fuel economy test is performed, the vehicle has to adapt within a few drive cycles. Further, the calibration must be matured rapidly enough for the vehicle to pass emissions.
In one example, some of the above issues may be addressed by a method for an engine system comprising: adjusting data points of a vehicle powertrain calibration table using data collected on-board a vehicle and using data downloaded from an off-board network, the downloaded data collected on-board one or more other vehicles communicating with the network. The off-board network may be, for example, a cloud computing system. In this way, cloud calibration may be advantageously used to more fully populate engine calibration tables in a shorter amount of time.
For example, a first phase of cloud calibration may be performed during vehicle development by the manufacturer to achieve rapid calibration to pass all emissions requirements. Therein, a calibration table for a new vehicle, such as a new type (make or model) or new family of vehicles may be developed. Therein, before sale of the vehicle to a consumer, the calibration table may be populated with calibration data collected on-board a fleet of vehicles of the same make and model being developed and calibrated by the manufacturer. The calibration data collected on-board each vehicle in the fleet of vehicles may be uploaded to a cloud computing system. A controller of the individual vehicle may download the relevant data and rapidly update an initial calibration table of the vehicle.
A second phase of cloud calibration may be performed after the vehicle is in the customer's hands to further optimize vehicle performance for fuel economy, emissions, and driveability. The second phase of cloud calibration also accounts for component aging, wear and, enables diagnostic routines to be triggered as needed. Therein, the initial calibration table (the table the vehicle initially came with) may be updated. For example, a fleet of vehicles in use by respective consumers may collect calibration data under various operating conditions while traveling on the road. The calibration data from each vehicle of the fleet may be uploaded to a cloud computing system and stored there. In addition, calibration data for individual vehicles may be stored on their respective controller's memory. Each vehicle may then advantageously download data generated on-board other vehicles with matching powertrain characteristics to adapt or update their respective calibration tables. For example, a first vehicle may have sufficient on-board generated data corresponding to a first region of the given vehicle's powertrain calibration table. Accordingly, the vehicle's controller may populate the first region of the calibration table with the on-board collected data. Sufficient on-board data may be generated due to the first vehicle spending more than a threshold amount of time at operating conditions (e.g., speed-load conditions) corresponding to the first region of the calibration table. However, the first vehicle may have insufficient on-board generated data corresponding to a second, different operating region of the calibration table. Insufficient data may be generated due to the vehicle spending less than the threshold amount of time at operating conditions corresponding to the second region of the calibration table. Therefore, the controller may identify one or more other vehicles, such as a second vehicle, in the fleet having matching powertrain characteristics and whose calibration table has sufficient data populating the second region of the calibration table. As such, the discrepancy may be due to differences in driving habits between the operators of the first vehicle and the second vehicle. For example, the operator of the first vehicle may tend to perform longer highway trips while the operator of the second vehicle may tend to perform shorter in-city trips. Thus, while the first vehicle may have higher residence times at high speed and high load conditions, the second vehicle may have higher residence times at low speed and high load conditions. The controller of the first vehicle may then download the data collected on-board the second vehicle to populate the second region of its calibration table. Vehicle actuator adjustments for the first vehicle may then be performed based on the updated calibration table. Thus, the calibration table of a vehicle operated for long distance trips on the highway may be matured with data captured on a vehicle operated for short within city trips. As another example, the calibration tables of a vehicle operated in hot and dry weather conditions may be adjusted or “matured” with data captured on a vehicle operated in hot and humid weather conditions. As such, data pertaining to many different aspects of vehicle performance and adaptation may be used to improve vehicle operation.
In this way, full engine maps covering a larger number of degrees of freedom can be generated faster. By relying on data captured on-board one or more other vehicles having matching characteristics, calibration data pertaining to operating conditions and driving maneuvers not frequently experienced on a given vehicle can be imported from the other vehicles. By using the global data to populate the majority of operating conditions of a vehicle's calibration table, vehicle performance at those conditions can be improved. In addition, local adaptations can be used to fine tune the vehicle's performance. As such, this enables an average adaptation estimate to be provided faster while adapting for piece-to-piece variation by the individual vehicle more quickly. In addition, by using the data from one or more other vehicles during an initial phase of calibration table development, enough data samples may be provided for substantially all the speed-load points required for emissions testing. As such, this improves the confidence level of the data populating the vehicle's calibration table and increases the likelihood of the vehicle passing an emissions test. Overall, engine and powertrain calibration accuracy is improved, improving vehicle performance.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.